High versus low added sugar consumption for the primary prevention of cardiovascular disease

Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To assess the effects of a high versus low added sugar consumption for primary prevention of CVD in the general population.

Background

High intake of added sugars has been suggested as having an impact on risk for cardiovascular disease (CVD). Many studies support this claim, in particular, in regards to sugar‐sweetened beverages. However, for total added sugar intake, the evidence is much weaker. Hence, there is a need to review the current evidence from randomised controlled trials (RCTs) in this area.

Description of the condition

Cardiovascular diseases (CVDs) are disorders of the heart and blood vessels and incorporate, for instance, acute coronary and cerebrovascular events. The origin for many CVDs is atherosclerosis, a condition that develops over many years and is often initially asymptomatic. Atherosclerosis is defined as the progressive accumulation of lipids and inflammatory cells in arteries of the body (Torres 2015). Atherosclerosis develops when arteries are narrowed or completely blocked, thus restricting the flow of blood or oxygen to organs or tissue. CVDs are a major cause of disability and are the number one cause of death globally. In 2015, it was estimated that 31% of all global deaths were due to CVDs (World Health Organization 2017). As the world's population increases and becomes older, the prevalence of CVDs is expected to increase further in the future (Roth 2015).

The most established modifiable risk factors for CVDs are physical inactivity, tobacco use, excessive alcohol consumption and maintaining an unhealthy diet. Non‐modifiable factors that can affect CVD risk include: higher age, male sex and heredity (Mendis 2011). In order to decrease mortality and morbidity resulting from CVDs, early detection methods as well as prevention strategies focusing on the modifiable risk factors are crucial. Prominent markers of detection and treating targets for CVDs are overweight and obesity, dyslipidaemia  and hypertension (Mendis 2011), which all are becoming increasingly prevalent health issues worldwide (Global Burden of Disease 2016).

Description of the intervention

Sugars (all mono‐ and disaccharides) can be divided into two categories: those occurring naturally in foods (intrinsic), such as lactose in milk and fructose in fruit, and those that are added to foods (extrinsic). There is no globally unanimous way to define the term 'added sugar', but the definition by the American Heart Association is clear, simple and corresponds to the majority of other definitions: "Added sugars include any sugars or caloric sweeteners that are added to foods or beverages during processing or preparation", for instance, white sugar (sucrose), honey or high fructose corn syrups (HFCS) (American Heart Association 2017). The World Health Organisation (WHO) utilises the term 'free sugars' which includes all added sugars and the naturally occurring sugars in fruit juices (World Health Organization 2015). The WHO recommends that, for both children and adults, the total intake of free sugars should be less than 10% of the total energy intake and, ideally, not exceed 5%. It is a growing public health concern that high added sugar intake is causing an increased overall intake of calories for many individuals. Also of concern is that a high intake of sugar‐rich foods can replace healthier and more nutritious foods, resulting in a less nutrient‐dense diet. Impaired nutrient density and the effects on increased energy intake and body weight, as well as the risk for dental caries, lies at the foundation of today's dietary guidelines regarding sugar (World Health Organization 2015).

The consumption of added sugar in high‐income countries is, in general, exceeding this recommended upper level. However, the current overall trends in these countries are actually suggesting a reached plateau or even a small decrease (Wittekind 2014), while the global trends, driven by low‐ and middle‐income countries, are suggesting overall increasing sugar consumption (World Cancer Research Fund International 2015). 

How the intervention might work

One of the main driving forces behind why high sugar intake is hypothesised to increase risk of CVD is because of the potential weight gain it is causing. Sugar‐rich products are generally very palatable, or “high rewarding” (Stice 2013), and at the same time less satiating than more fibrous foods (Rebello 2013). This can result in over‐consumption and an energy intake higher than required, hence leading to weight gain. In a meta‐analysis of RCTs and cohort studies on sugar intake and body weight, of a total of 38 cohort studies and 30 trials, the authors concluded that sugar intake is a determinant of body weight, which likely is due to skewed energy balance rather than any biological effects of the sugar molecules. However, the effects of a reduced sugar intake have modest effects on obesity (Te Morenga 2012).

Higher sugar intakes are also associated with increased triglycerides, total cholesterol and LDL‐cholesterol in a large meta‐analysis of RCTs (Te Morenga 2014), which is considered to be a  serum lipid profile that induces atherosclerosis (NECP 2002). In addition, it is suggested that sugar intake can be related to CVD risk through decreasing insulin sensitivity (Aeberli 2011) and can possibly also promote low‐grade inflammation (Aeberli 2011; Liu 2002; Sorensen 2005). However, several studies do not support these findings (Black 2006; Lewis 2013; Maersk 2012) and it is difficult to say with certainty whether this is a cause of the actual sugar itself or from altered adipose tissue (body fat) and blood lipid profile. In addition, blood pressure is one of strongest risk factors and first treatment targets for CVD, and is therefore also considered an important risk marker to evaluate (Karmali 2018).

Fructose has been in particular focus in a lot of research, possibly because of the large increase in usage of HFCS for sweetening, which contains 55% fructose and 45% glucose. Fructose has a lower glycaemic index (GI) compared to both sucrose and especially glucose, and does not yield as high insulin responses (Bantle 1986; Teff 2009), but there are findings suggesting that fructose, in particular, contributes to the increased accumulation of lipids in both vessels and liver and to visceral accumulation of adipose tissue surrounding the organs (Stanhope 2009; Silbernagel 2011). However, fructose and glucose are seldom consumed separately, but rather joined as sucrose or HFCS, and findings are not unanimous. Thus, it remains unclear how much the different metabolic pathways of glucose and fructose actually matter for the adipose tissue, blood lipid profile and cardiovascular risk (Silbernagel 2011). Therefore, this review will focus on different intake levels of all added sugars rather than comparing different sugars against each other.

Why it is important to do this review

Regarding diet and CVD prevention, meta‐analyses of either RCTs, cohort studies, or both, have previously shown that low sodium intake (Aburto 2013), low intakes of saturated fatty acids (Hooper 2015), high intake of dietary fibre (Threapleton 2013) and whole grains (Ye 2012) and a diet rich in fruits and vegetables (Hartley 2013) can decrease CVD risk. As for sugars, evidence from a meta‐analysis of prospective cohort studies on sugar‐sweetened beverages and its effect on increased cardiovascular risk is quite conclusive (Xi 2015). However, regarding total added sugar intake, two cohort studies have observed a positive relationship between added sugar intake and CVD mortality (Yang 2014; Ramne 2018). However, the study by Ramne and colleagues could also see tendencies for increased risk with the lowest intakes, i.e. a U‐shaped association (Ramne 2018). A third cohort study investigating sugar and mortality, could not see an association (Tasevska 2014). Additionally, both the study by Ramne and colleagues and Tasevska and colleagues could see tendencies for opposite associations when separately examining added sugar intake from beverages (positive) and solid foods (negative). This inconclusive epidemiology goes in line with the existing evidence for total added sugar intake and CVDs, which currently is not very persuasive or conclusive (EFSA 2010; Hauner 2012).

As mentioned, the previous focus in research has primarily been on sugar‐sweetened beverages, where the majority of data comes from observational studies, or comparing different types of sugar against each other, i.e. glucose versus fructose, rather than investigating an altered dose of total added sugars. (Chiavaroli 2015; Evans 2017; Ha 2012; Zhang 2013). Several Cochrane reviews of RCTs investigating different dietary components for prevention of CVD have been conducted (Clar 2017; Hartley 2016; Hooper 2018; Kelly 2017), however, none regarding added sugar intake. A few other systematic reviews have investigated added sugar intake and cardiovascular risk markers, but none have aimed to include CVD incidence as the primary outcome (Fattore 2017; Te Morenga 2014). Hence, it is of great importance to elucidate the evidence from RCTs that attend to the whole spectra of added sugar consumption from all sources and its potential role in primary prevention of CVD.

Objectives

To assess the effects of a high versus low added sugar consumption for primary prevention of CVD in the general population.

Methods

Criteria for considering studies for this review

Types of studies

We will include randomised controlled trials (RCTs), including cross‐over trials and cluster RCTs. We will include studies reported as full‐text, those published as abstract only, and unpublished data.

Types of participants

The study participants are required to have participated in an intervention comparing different levels of added sugar intake. Studies will not be included if they fulfil one or more of the following criteria:

• Study population aged below 18 years;

• Study population with diabetes mellitus (type 1 and 2);

• Study population with previous CVD.

If only a subset of the population has any of the exclusion criteria, we will try to contact the trialists and obtain the information on the subsets of the study population. If this is not possible, a cut‐off of 10% will be used, and if the share of study participants who are aged below 18 years, have diabetes mellitus or previous CVD is exceeding this number, the study will be excluded.

Types of interventions

We will include trials aimed to manipulate sugar intake, hence we will compare a high sugar intake with a low sugar intake. The studies can include any type of added sugar (e.g. sucrose, fructose or glucose) or sugar‐rich foods and beverages consumed orally. Limiting to added sugars means that interventions of, for instance, high or low fruit intake (high in intrinsic sugars) will not be included. The included interventions can consist of dietary advice where added sugar intake is either increased or decreased, supplementation with sugar, providing of sugar‐rich products or encompass a diet alteration where added sugar intake is either at a high or low level (as defined in the individual studies). If the intervention is aimed towards increasing sugar intake, then the comparison group (or no treatment group) represents the low sugar group. And if the intervention group aims to decrease sugar intake, then the comparison group (or no treatment group) represents the high sugar group. 

The comparison group can be given no advice/supplementation (continue with their usual diet), a placebo product or any control diet. Studies that have compared corresponding amounts of different types of sugars, for example glucose versus fructose, will be excluded from the review. We will not include studies where an altered sugar consumption is part of an overall diet or lifestyle intervention, which, for instance, additionally modifies exercise routines, in order to avoid potential confounding.

In order to include as many studies as possible, while still ensuring enough time for stabilisation of, for instance, blood lipid concentrations, we will include interventions that have lasted a minimum of four weeks between baseline and follow‐up. Four weeks is not expected to be enough time to have an effect on our primary outcomes, therefore, a minimum of 6 months follow‐up time is required for these outcomes. This will enable us to efficiently synthesise the existing evidence. Cross‐over trials will be included, but only data from the first period will be extracted for evaluation of the primary outcomes, although from both periods for the secondary outcomes. A minimum wash‐out period of two weeks is necessary to avoid carry‐over effects for the secondary outcomes.

Types of outcome measures

Primary outcomes

• Incident cardiovascular event (coronary, carotid, cerebral and peripheral arterial disease)

• All‐cause mortality

Secondary outcomes

• Changes in systolic blood pressure

• Changes in diastolic blood pressure

• Changes in total cholesterol

• Changes in LDL‐cholesterol

• Changes in HDL‐cholesterol

• Changes in triglycerides

• Changes in fasting plasma glucose

• Adverse events: gastrointestinal symptoms (such as nausea, abdominal pain, constipation and diarrhoea)

• Adverse events: impaired dental health (such as dental caries)

Where a published report does not appear to report one of these outcomes, we will access the trial protocol and contact the trial authors to ascertain whether the outcomes were measured but not reported. Relevant trials which measured these outcomes but did not report the data at all, or not in a usable format, will be included in the review as part of the narrative.

Search methods for identification of studies

Electronic searches

We will identify trials through systematic searches of the following bibliographic databases:

• Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library;

• MEDLINE (Ovid, from 1946 onwards);

• Embase (Ovid, from 1980 onwards);

• Conference Proceedings Citation Index ‐ Science (CPCI‐S; Web of Science, from 1990 onwards).

The preliminary search strategy for MEDLINE (Ovid) (Appendix 1) will be adapted for use in the other databases. The Cochrane sensitivity‐precision maximising RCT filter (Lefebvre 2011) will be applied to MEDLINE (Ovid) and adaptations of it to the other databases, except CENTRAL. We will also conduct a search of ClinicalTrials.gov (www.ClinicalTrials.gov) and the WHO International Clinical Trials Registry Platform (ICTRP) Search Portal (http://apps.who.int/trialsearch/) for ongoing or unpublished trials.

We will impose no restriction on language of publication or publication status. We will not perform a separate search for adverse effects of interventions of altered sugar intake. We will consider adverse effects described in included studies only.

Searching other resources

We will check reference lists of all included studies and any relevant systematic reviews identified for additional references to RCTs. We will also examine any relevant retraction statements and errata for included studies. We will contact authors for missing data, if necessary.

Data collection and analysis

Selection of studies

Two review authors (SB and SR) will independently screen titles and abstracts for inclusion of all the potential studies we identify as a result of the search and code them as 'retrieve' (eligible or potentially eligible/unclear) or 'do not retrieve'. If there are any disagreements, a third and fourth author will be asked to arbitrate (SA and ES). We will retrieve the full‐text study reports/publication and two review authors (SB and SR) will independently screen the full‐text and identify studies for inclusion, and identify and record reasons for exclusion of the ineligible studies. We will resolve any disagreement through discussion or, if required, we will consult a third and fourth person (SA and ES). We will identify and exclude duplicates and collate multiple articles of the same study so that each study, rather than each article, is the unit of interest in the review. We will record the selection process in sufficient detail to complete a PRISMA flow diagram and 'Characteristics of excluded studies' table.

Data extraction and management

We will use a data collection form for study characteristics and outcome data which has been piloted on at least one study in the review. We will extract the following study characteristics.

• Methods: study design, total duration of study, number of study centres and location, study setting, date of study and wash‐out period for cross‐over trials.

• Participants: N randomised, N lost to follow‐up/withdrawn, N analysed, mean age, age range, gender, mean BMI, parameters of metabolic syndrome, other diseases, eventual weight change, compliance to intervention, inclusion criteria, and exclusion criteria. Baseline data on lipids and other characteristics will be collected and assessed for imbalance between groups.

• Interventions: intervention details, and comparison details in sufficient detail to allow subgroups to be determined.

• Outcomes: primary and secondary outcomes (including adverse events) specified and collected, and time points reported.

• Notes: funding source of trial and notable conflicts of interest of trial authors.

Two review authors (SB and SR) will independently extract outcome data from included studies. We will resolve disagreements by consensus or by involving a third and fourth person (SA and ES). One review author (SB) will transfer data into the Review Manager (RevMan 2014) file. We will double‐check that data is entered correctly by comparing the data presented in the systematic review with the data extraction form. Two review authors (SB and SR) will spot‐check study characteristics for accuracy against the trial report.

Assessment of risk of bias in included studies

Two review authors (SB and SR) will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will resolve any disagreements by discussion or by involving two other authors (SA and ES). We will assess the risk of bias according to the following domains.

• Random sequence generation.

• Allocation concealment.

• Blinding of participants and personnel.

• Blinding of outcome assessment.

• Incomplete outcome data.

• Selective outcome reporting.

• Other bias.

Each potential source of bias will be graded as high, low or unclear and provide a quote from the study report together with a justification for our judgment in the 'Risk of bias' table. Randomisation, concealed allocation, selective outcome reporting and one other domain need to be at low risk of bias in order for a study to be considered at low summary risk of bias. The risk of bias judgements will be summarised across different studies for each of the domains listed. Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the 'Risk of bias' table. The primary 'other biases' to consider are: compliance to the intervention and vested interests. When considering treatment effects, the risk of bias will take into account the studies that contribute to that outcome.

If we identify relevant cluster‐randomised studies, we will consider the same bias domains as for the standard RCTs, and also consider the following biases:

• recruitment bias.

• baseline imbalance.

• loss of clusters.

• incorrect analysis.

• comparability with individually randomised trials.

If we identify cross‐over trials, the risk of biases to consider are:

• whether the cross‐over design is suitable.

• whether there is a carry‐over effect.

• whether only first‐period data are available.

• incorrect analysis.

• comparability of results with those from parallel‐group trials.

Assessment of bias in conducting the systematic review:

The review will be conducted according to this published protocol and we will report any deviations from it in the 'Differences between protocol and review' section of the systematic review.

Measures of treatment effect

Dichotomous data will be analysed as risk ratios with 95% confidence intervals. If we find studies expressing effect sizes in different risk estimates, such as odds ratios, these will be converted into risk ratios using the Generic Inverse Variance (GIV) method. Mean difference will be used, as far as possible, for continuous data.  If the same study presents both change scores and end of follow‐up data, we will use the Cochrane Handbook (Deeks 2018) guidelines to identify the most appropriate form to include. Differences in units across studies will be converted to the unit most used to avoid expressions in standardised mean difference, which only will be used when it is absolutely necessary. We will enter data presented as a scale with a consistent direction of effect. We will describe narratively skewed data reported as medians and interquartile ranges.

Unit of analysis issues

If studies report results at several time points, we will analyse the time point with the longest follow‐up.

Studies with multiple intervention groups: If we identify relevant studies with multiple intervention groups, we will combine the intervention arms to create a single intervention arm in order to avoid splitting the control group. Moreover, since it might be different between studies if the intervention arm aims to increase or decrease sugar intake, these will be combined in the main analyses. This will allow us to always compare low sugar intake with high sugar intake so that trials aiming to increase sugar intake will appear with the control arm in the low sugar intake side of the equation. 

Cross‐over trials: We will evaluate the evidence based on data from the first period of the intervention for the primary outcomes and any dichotomous secondary outcomes, and both periods for the continuous secondary outcomes, so long as there is a minimum wash‐out period of two weeks between phases. We will adhere to the recommended methods outlined in the Cochrane Handbook regarding cross‐over trials, and these recommendations will form the basis of the decisions regarding how to analyse the results and how to determine if the results are suitable to include in a meta‐analysis (Higgins 2011).

Cluster‐randomised trials: In regards to cluster‐randomised trials, we will analyse these by using the cluster (unit of randomisation) as the number of observations. If it is required, the individual level means and standard deviations adjusted for clustering will be utilised together with the number of clusters in the denominator to appropriately weight the trials. If we encounter a relevant cluster‐randomised trial, we will consult a statistician to ensure that the trialists have used appropriate methods. If the trialists have adjusted for clustering and analysed the results properly, we will include the results using the GIV method. If not, we will attempt to estimate the effective sample size, provided that the intra‐cluster correlation coefficient is reported.

Dealing with missing data

We will contact study authors in order to obtain missing numerical outcome data where possible (e.g. when a study is identified as abstract only). If this is not possible, the 'risk of bias' assessment will reflect this potential shortcoming. If relevant trials do not report the SD, we will use the Revman calculator to attain the SD, from other available data, such as standard error (SE), 95% confidence interval (CI) and P values. For missing participant data, we will base the evidence on an intention‐to‐treat analysis.

Assessment of heterogeneity

We will use the I² statistic to measure heterogeneity among the trials in each analysis. If we identify substantial heterogeneity, we will report it and explore possible causes by prespecified subgroup analysis. We will also visually assess forest plots in order to evaluate the direction and magnitude of effects and potential overlap of CIs. Statistical heterogeneity will be assessed using the I² test. We will, in addition to using the I² test, also consider the P value from the Chi² tests. The significance level is set at a P value < 0.05. I² will be calculated using a random‐effects model and interpreted according to the Cochrane Handbook (Deeks 2018) guidelines:

• 0% to 40%: might not be important;

• 30% to 60%: may represent moderate heterogeneity:

• 50% to 90%: may represent substantial heterogeneity:

• 75% to 100%: considerable heterogeneity.

Assessment of reporting biases

If we are able to pool more than 10 trials, we will create and examine a funnel plot to explore possible small study biases for the primary outcomes.

Data synthesis

We will undertake meta‐analyses only where this is meaningful i.e. if the treatments, participants and the underlying clinical question are similar enough for pooling to make sense.

We will use a random‐effects model, as we are expecting some degree of heterogeneity in the results. Clinical diversity among the included studies is expected, since they could have varying interventions, doses and comparisons. In addition, previous reviews that have a similar approach and focus, have chosen the random‐effects model.

Where there were considerable baseline imbalances in small studies, studies may report outcomes that have been adjusted for these, for example, by using analysis of covariance (ANCOVA). We will consider including these in the meta‐analysis where feasible, using the generic inverse‐variance method.

If some studies report continuous outcomes using change from baseline and some report end of follow‐up values, we will combine these in the same meta‐analysis if MD can be used, and group them separately so that it is clear to the reader which are change scores and which are end of follow‐up scores. If SMD are required, we will not pool change from baseline data with end of follow‐up data.

'Summary of findings' table

We will create a 'Summary of findings' table for high versus low added sugar intake, presenting the following outcomes: cardiovascular events, all‐cause mortality, systolic blood pressure, total cholesterol, LDL‐cholesterol, HDL‐cholesterol, and fasting plasma glucose. We will use the five GRADE considerations (study limitations, consistency of effect, imprecision, indirectness and publication bias) to assess the quality of a body of evidence as it relates to the studies which contribute data to the meta‐analyses for the prespecified outcomes. We will use methods and recommendations described in Section 8.5 (Higgins 2011) and Chapter 15 (Schünemann 2018) of the Cochrane Handbook for Systematic Reviews of Interventions using GRADEpro software (https://gradepro.org/).

We will justify all decisions to downgrade the quality of studies using footnotes and we will make comments to aid the reader's understanding of the review, where necessary.

Judgments about evidence quality will be made by two review authors (SB and SR) working independently, with disagreements resolved by discussion or involving a third and fourth author (SA and ES). Judgments will be justified, documented and incorporated into reporting of results for each outcome.

We plan to extract study data, format our comparisons in data tables and prepare the 'Summary of findings' table before writing the results and conclusions of our review. A template 'Summary of findings' table is included as Table 1.

Table 1.

Draft 'Summary of Findings' Table: High versus low added sugar intake  for the primary prevention of CVD

High versus low added sugar intake  for the primary prevention of CVD
Patient or population: adults without diabetes or CVD Setting: community or controlled environment Intervention: high added sugar intake Comparison: low added sugar intake
Outcomes	Anticipated absolute effects* (95% CI)		Relative effect (95% CI)	№ of participants (studies)	Certainty of the evidence (GRADE)	Comments
	Risk with control	Risk with treatment
Cardiovascular event
All‐cause mortality
Systolic blood pressure
Total cholesterol
LDL‐ cholesterol
HDL‐cholesterol
Fasting plasma glucose
*The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). CI: Confidence interval; RR: Risk ratio; OR: Odds ratio;
GRADE Working Group grades of evidence High certainty: We are very confident that the true effect lies close to that of the estimate of the effect Moderate certainty: We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different Low certainty: Our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect Very low certainty: We have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect

Subgroup analysis and investigation of heterogeneity

We plan to carry out the following subgroup analyses:

• Increased or decreased added sugar intake;

• Isocaloric exchange or ad libitum intake;

• Liquid or solid state of added sugar source;

• Control in form of starch/refined grains;

• Weight change (> 0.5 kg difference in weight change between the two study arms) or no weight change;

• Healthy population or high risk population (metabolic syndrome or obesity, hypertension, dyslipidaemia and elevated fasting blood glucose levels/pre‐diabetes);

• Duration of intervention (more or less than 6 months);

• Industry funded studies or no involvement with industry.

We will investigate all outcomes in all subgroup analyses.

We will use the formal test for subgroup interactions in Review Manager (RevMan 2014). These are the potential a priori subgroup analyses; others may be added (for instance, different types of controls).

Sensitivity analysis

We plan to carry out the following sensitivity analyses:

• Only including studies with a low risk of bias. A study will be judged to be at low risk of bias when there is low risk in at least four domains (not including 'other bias'). Randomisation, concealed allocation and selective outcome reporting are of biggest concern in this review.

• Excluding cross‐over and cluster‐randomised trials.

These are the potential a priori sensitivity analyses; others may be added.

Reaching conclusions

We will base our conclusions only on findings from the quantitative or narrative synthesis of included studies for this review. We will avoid making recommendations for practice and our implications for research will suggest priorities for future research and outline what the remaining uncertainties are in the area.

Appendices

Appendix 1. Draft search strategy – MEDLINE

1     exp Dietary Sugars/ (4507)

2     sugar*.tw. (112607)

3     exp Monosaccharides/ (336823)

4     exp Disaccharides/ (57955)

5     monosaccharide*.tw. (10332)

6     disaccharide*.tw. (10705)

7     glucose.tw. (433808)

8     galactose.tw. (29478)

9     fructose.tw. (28597)

10     dextrose.tw. (11561)

11     sucrose.tw. (62674)

12     lactose.tw. (20208)

13     maltose.tw. (9448)

14     corn syrup.tw. (615)

15     honey.tw. (9800)

16     exp Sweetening Agents/ (219773)

17     sweetener*.tw. (3570)

18     sweetening.tw. (503)

19     maize syrup*.tw. (3)

20     glucose‐fructose syrup*.tw. (9)

21     isoglucose.tw. (5)

22     Carbonated Beverages/ (2673)

23     ((soft or fizzy or carbonated) adj3 drink*).tw. (3776)

24     or/1‐23 (799694)

25     exp Cardiovascular Diseases/ (2257777)

26     cardio*.tw. (699382)

27     cardia*.tw. (566120)

28     heart*.tw. (783635)

29     coronary*.tw. (371024)

30     angina*.tw. (52467)

31     ventric*.tw. (383248)

32     myocard*.tw. (359210)

33     pericard*.tw. (38902)

34     isch?em*.tw. (351300)

35     emboli*.tw. (114811)

36     arrhythmi*.tw. (84127)

37     thrombo*.tw. (337874)

38     atrial fibrillat*.tw. (61689)

39     tachycardi*.tw. (56602)

40     endocardi*.tw. (46519)

41     (sick adj sinus).tw. (2178)

42     exp Stroke/ (120536)

43     (stroke or stokes).tw. (228040)

44     cerebrovasc*.tw. (49752)

45     cerebral vascular.tw. (5600)

46     apoplexy.tw. (2897)

47     (brain adj2 accident*).tw. (157)

48     ((brain* or cerebral or lacunar) adj2 infarct*).tw. (24437)

49     exp Hypertension/ (244094)

50     hypertensi*.tw. (401940)

51     peripheral arter* disease*.tw. (12113)

52     ((high or increased or elevated) adj2 blood pressure).tw. (31069)

53     exp Hyperlipidemias/ (63798)

54     hyperlipid*.tw. (28767)

55     hyperlip?emia*.tw. (2389)

56     hypercholesterol*.tw. (32839)

57     hypercholester?emia*.tw. (683)

58     hyperlipoprotein?emia*.tw. (4280)

59     hypertriglycerid?emia*.tw. (11982)

60     exp Arteriosclerosis/ (166259)

61     Cholesterol, Dietary/ (8260)

62     (diet* adj2 cholesterol*).tw. (9991)

63     Cholesterol, HDL/ (27075)

64     (HDL* adj3 cholesterol*).tw. (40955)

65     (high density adj2 cholesterol*).tw. (27000)

66     alpha lipoprotein cholesterol.tw. (66)

67     Cholesterol, LDL/ (25796)

68     (LDL adj2 cholester*).tw. (34939)

69     (low density adj2 cholesterol*).tw. (25249)

70     beta lipoprotein cholesterol.tw. (73)

71     "coronary risk factor* ".tw. (3280)

72     Blood Pressure/ (266260)

73     blood pressure.tw. (276140)

74     exp Triglycerides/ (73886)

75     triglyceride*.tw. (103843)

76     triacylglycerol*.tw. (15250)

77     exp Lipoproteins/ (140822)

78     lipoprote*.tw. (139426)

79     "fasting blood sugar".tw. (2065)

80     "fasting plasma glucose".tw. (11492)

81     or/25‐80 (3854438)

82     diet/ (149297)

83     diet*.tw. (517566)

84     consum*.tw. (423025)

85     eat*.tw. (88232)

86     food*.tw. (415815)

87     nutri*.tw. (361108)

88     intake.tw. (245935)

89     or/82‐88 (1518310)

90     24 and 81 and 89 (39750)

91     randomized controlled trial.pt. (478613)

92     controlled clinical trial.pt. (92989)

93     randomized.ab. (438208)

94     placebo.ab. (196400)

95     clinical trials as topic.sh. (186436)

96     randomly.ab. (307855)

97     trial.ti. (196134)

98     91 or 92 or 93 or 94 or 95 or 96 or 97 (1205596)

99     exp animals/ not humans.sh. (4562400)

100     98 not 99 (1108767)

101     90 and 100 (6399)

What's new

Date	Event	Description
7 June 2019	Amended	A technical fix to display the downloadable pdf correctly has been applied.

Contributions of authors

Sara Nordkvist wrote the protocol draft and contributed to all stages of the protocol and search strategy development.

Stina Ramne wrote the protocol draft and contributed to all stages of the protocol and search strategy development.

Emily Sonestedt reviewed and contributed to all stages of the protocol and search strategy development.

Stefan Acosta reviewed and contributed to all stages of the protocol and search strategy development.

Sources of support

Internal sources

• No sources of support supplied

External sources

• This project was supported by the National Institute for Health Research, via Cochrane Infrastructure funding to the Heart Group. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Systematic Reviews Programme, NIHR, NHS or the Department of Health, UK.

Declarations of interest

ES has got financial support from the Swedish Nutrition Foundation (SNF), for giving presentations regarding scientific evidence of sugar consumption on health. SNF is a non‐profit organization that stimulates collaboration between academia and the food industry. It is owned by participating organisations (including food companies). The financial support is not related to the work for this review.

The rest of the authors declare that they have no competing interests.

Edited (no change to conclusions)